Biometric feature extraction using multiple image instantiations

ABSTRACT

Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated. Thus, the methodology eliminates false identifications of specific instances of biometric features while accentuating true identifications.

RELATED APPLICATION

This application claims the benefit of U.S. Provisional Application No.61/348,814, filed on May 27, 2010, titled “Robust Biometric FeatureExtraction using Multiple Image Instantiations,” the entirety of whichprovisional application is incorporated by reference herein.

FIELD OF THE INVENTION

The invention relates generally to person identification. Morespecifically, the invention relates to systems and methods of biometricfeature extraction from image instantiations.

BACKGROUND

Fingerprints, an example of which appears in FIG. 1, are a powerfulbiometric because of the uniqueness and stability of the featuresignature of the human fingerprint pattern. The last decade has seen animprovement in the ability of automated image analysis systems toextract features from fingerprint patterns quickly, accurately, andconsistently. All of these factors have contributed to the success offingerprints as a biometric for person identification systems.

Person identification using fingerprints involves several steps. A firststep involves fingerprint image acquisition, with the goal being theaccurate reproduction of the fingerprint pattern in digital image form.A subsequent step is the accurate extraction of fingerprint features,known to be unique for every individual, from the digital image. In alater step, the pattern of these features is used to search through adatabase of patterns to determine the optimal match, and hence, toidentify the correct individual.

The features used for identification are known as Galton details orminutiae, and relate to the location of points centered on specificpatterns formed by the ridges, which appear as black lines in thefingerprint image of FIG. 1, and valleys, which appear as white lines.The comparison of the relative positions of these points with areference fingerprint determines the degree to which a given unknownpattern matches the reference fingerprint.

There is a variety of different types of minutiae. FIG. 2 shows four ofthe most common types, isolated by encircling in white: A) Island; B)Dot; C) Bifurcation; and D) Ending Ridge. An average fingerprint mayhave 20 to 40 minutia points, although fingerprints of poor quality mayhave as few as 3 or 4, and fingerprints that are “rolled” to imprintmore surface area of the finger may have as many as 100 or more. Thespecific two-dimensional layout of the minutia points uniquelycharacterizes an individual. Clearly, the more minutia points that arecorrectly located, the greater the probability that a given fingerprintwill be accurately matched against its reference fingerprint in a givendatabase. The goal in biometric systems is to maximize that probability;therefore, accurate feature extraction is central to this goal.

The examples of FIG. 2 seem to be quite clear. However, in practice,fingerprint images are significantly more degraded than those shown inFIG. 2 and the location (and even the type) of minutiae much moreambiguous. FIG. 3 shows examples of real minutiae and demonstrates theinherent uncertainty in an image, leading to problematic decisions forboth experts and automated algorithms. For example, in FIG. 3, themagnified area 2 contains numerous points in the image that might beconsidered as ending ridges or dots. However, expert examination in themagnified area reveals only two minutiae: minutia point 4, which is adefinite bifurcation 6; and minutia point 8 that, despite carefulexamination, is ambiguous, especially if the larger context of a ridgeflow is considered. In fact, minutia point 8 can be considered abifurcation 10 or an ending ridge (or even dot) 12 depending on whetherthe break in the arm is due to poor image quality or due to a realphysical break. In other words, it is unknown whether the break is aphysical characteristic of the fingerprint or an artifact of the imageacquisition/analysis process because of inherent uncertainty in theimage itself.

In instances of ambiguity, minutia extraction algorithms may do one offour things: 1) correctly locate a true minutia, yielding optimalresults; 2) fail to locate a true minutia, which can weaken theprobability of a subsequent match; 3) incorrectly locate a falseminutia, which can later confuse the matching algorithm; or 4) correctlylocate a true minutia but misidentify the minutia type, resulting in aminor position offset that can sometimes appear as a missed true minutiaand an incorrectly located false minutia (a hybrid of (2) and (3)—bothweakening the probability of a match and confusing the matchingalgorithm).

The acquisition of fingerprint images of fingerprints can occur in manydifferent ways. Irrespective of how the image is acquired, however, theimage formation process is known to result in an inherently flawedrecreation of the actual fingerprint. The flawed recreation occursbecause of several reasons:

1. Image Deformation. Most fingerprint scanning devices require thesubject either to press their finger onto a platen or to brush theirfinger against a scanning device. Because of the elasticity of skin andvarying quantity of finger pressure, in both cases the fingerprintpattern can be slightly deformed, with slightly different deformationswith each image acquisition.

2. Image Superposition. Dirty or oily fingers can leave behind residualfingerprints on a platen. If the platen is not cleaned between scans, asis too often the case, images of these residual prints can superimposethemselves on the scanned fingerprint.

3. Image Distortion. The image acquisition process relies on some methodthat measures the physical differences between ridges and valleys.Whether that means measuring capacitance, reflected sound, reflectedradiation emitted radiation, or the like, the projection of an irregular3-D object (the finger) onto a 2D flat plane inevitably introduces imagedistortions.

4. Image Resolution. Because of the relatively small size of minutiae,image resolution is a critical factor in facilitating accurate automaticdetection. Higher resolution can yield superior definition, but inpractice, resolution is often limited by cost and the technologyavailable.

SUMMARY

In one aspect, the invention features a method of extracting biometricfeatures from images of a biometric identity. The method comprisesacquiring multiple different images of a same biometric identity,identifying specific instances of biometric features in each of themultiple different images of the same biometric identity, and mergingthe specific instances of biometric features identified in the multipledifferent images of the same biometric identity into a data record thatprovides a digital representation of the biometric identity.

In another aspect, the invention features a computer program product forextracting biometric features from images of a biometric identity. Thecomputer program product comprises a computer readable persistentstorage medium having computer readable program code embodied therewith.The computer readable program code comprises computer readable programcode configured to acquire, if executed, multiple different images of asame biometric identity, computer readable program code configured toidentify, if executed, specific instances of biometric features in eachof the multiple different images of the same biometric identity, andcomputer readable program code configured to merge, if executed, thespecific instances of biometric features identified in the multipleimages of the same biometric identity into a data record that provides adigital representation of the biometric identity.

In still another aspect, the invention features a system for extractingbiometric features from images of a biometric identity. The systemincludes means for acquiring multiple different images of a samebiometric identity. A processor is programmed to run computer readableprogram code that identifies, if executed, specific instances ofbiometric features in each of the multiple different images of the samebiometric identity and merges the identified specific instances ofbiometric features into a data record that provides a digitalrepresentation of the biometric identity. Memory is configured to storea list of specific instances of biometric features that are being mergedinto the data record.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and further advantages of this invention may be betterunderstood by referring to the following description in conjunction withthe accompanying drawings, in which like numerals indicate likestructural elements and features in various figures. The drawings arenot necessarily to scale, emphasis instead being placed uponillustrating the principles of the invention.

FIG. 1 is an image of a fingerprint.

FIG. 2 is a set of images showing four different types of minutiaecommonly found in fingerprints.

FIG. 3 is an image of a fingerprint in which the ridges and valleys areless than optimally defined, demonstrating an inherent uncertainty in animage.

FIG. 4 is a flow chart of an embodiment of a general process ofgenerating a fingerprint template.

FIG. 5 is a flow chart of another embodiment of a process of generatinga fingerprint template from multiple instantiations.

FIG. 6 is a flow chart of still another embodiment of a process ofgenerating a fingerprint template from multiple instantiations.

FIG. 7 is a flow chart of an embodiment of a process of merging featureextraction data taken from multiple instantiations into a fingerprinttemplate.

FIG. 8 is a functional block diagram of a processor-based device thatcan perform biometric feature extraction based on multiple differentimages of the biometric identity and produce therefrom a data record.

DETAILED DESCRIPTION

Conventional systems focus on achieving robust, accurate, and consistentfeature extraction from a single image. Technological advancements andthe development of numerous standards have made image acquisition afairly robust and repeatable process, enabling relatively accuratematching among thousands and even millions of fingerprints. Not allfingerprint features are expected to be found in every fingerprintimage, but because of the large number of minutia in any singlefingerprint pattern, a large enough subset of features is often found toenable accurate matching to a degree, provided the quality of theoriginal fingerprint is sufficiently high. However, as the quality ofthe original fingerprint degrades, the detrimental effect upon matchingresulting from this lack of precise repeatability becomes moreegregious.

To address this issue, the biometric feature extraction systems andmethods described herein provide a methodology for improving therobustness, accuracy, and repeatability of feature extraction fromdigital images by combining information from the analysis of multipleinstantiations of the same biometric identity. Although describedprimarily in connection with fingerprint images, the principlesdescribed herein can be applied to other biometric identities,including, but not limited to eyes, handprints, footprints, palm prints,thumbprints, and faces.

The multiple instantiations of the same biometric identity can be theresult of a physical perturbation in the image formation process (e.g.,a slight change in finger pressure during fingerprint scanning) of asynthetic or simulated perturbation using image-processing techniques.Specific instances of biometric features are extracted from the multipledifferent images, identified, and merged into a data record, such as afingerprint template. In one embodiment, a fingerprint template consistsof minutia location and angle data, with sufficient information foreffective matching. The data record provides a digital representation ofthe biometric identity. The more accurate the digital representation,the more likely the biometric identity can be successfully matched.

In one embodiment, a counter is associated with each specific instanceof a biometric feature found in the multiple images. Specific instancesof biometric features found most frequently will have high counts andare indicative of true identifications; those with low counts areindicative of false identifications. Use of a threshold can distinguishbetween the true and false identifications; specific instances ofbiometric features with counts below the threshold are excluded when thedigital representation of the biometric identity (i.e., the data record)is generated. Accordingly, a methodology described herein operates toeliminate the false identifications while accentuating the trueidentifications.

FIG. 4 shows an embodiment of a general process 100 for generating afingerprint template, which is an example of a biometric data record.The process 100 involves a method of image instantiation, where an imageof a fingerprint is obtained (step 102), either from previously storedmedia or from a live imaging device (e.g., a scanner). A minutiaextractor extracts (step 104) relevant fingerprint features and thenformats (step 106) that data into a fingerprint template. Thefingerprint template can then be stored to a file, and/or used as aconcise substitute for the raw image in biometric applications.

FIG. 5 and FIG. 6 describe embodiments of processes for minutiaextraction based on the analysis of multiple instantiations of the samefingerprint identity. In each described process, the merging ofinformation obtained from the multiple instantiations produces robust,accurate, and repeatable minutia locations.

The general principles underlying the processes of FIG. 5 and FIG. 6 canbe explained with an analogy. Consider a large rigid sheet with dimplesthroughout, each dimple corresponding to a feature in the fingerprintpattern that either is a true minutia point or looks like a true minutiapoint. Then place a small marble in each dimple where a minutia has beenfound by a minutia extraction algorithm. In some cases, however, theremay be small marbles sitting on the lip of some of those dimples. These“empty” dimples represent minutiae that the initial minutia extractionalgorithm does not locate.

Jostling the rigid sheet by a small amount represents a “perturbation”of the rigid sheet. Because most minutia extraction algorithms aresophisticated enough to be able to locate minutia despite smallimperfections in the raw image data, most, if not all of the marbleswill remain in their dimples, despite the perturbation, as a testamentto the inherent stability and robustness of algorithm. In addition, areasonable assumption is that the chances of a marble being dislodgedfrom a dimple are much smaller than the chances of one falling into adimple from the dimple's lip. Thus, when the rigid sheet is jostled,some of those marbles sitting on the lip may fall into their nearbydimples, resulting in new minutia detection.

To continue with this analogy, note that the dimples corresponding totrue minutiae are “deeper” than the ones corresponding to falseminutiae, because the false minutiae are not real structures, but aconsequence of poor image formation. Consequently, perturbations arealso likely to dislodge marbles in the shallow dimples (i.e., falseminutia), essentially improving the likelihood of eliminating thedetection of false minutiae. The exact type of perturbation is not asimportant as the amount of perturbation.

FIG. 5 shows an embodiment of a process 120 for extracting minutiae byapplying simulated perturbations to a single image of a biometricidentity. The biometric feature extraction device receives (step 122) aninput image. The input image may be obtained from an existing databaseor file system or acquired in real time from an image-acquisitiondevice, such as a scanner. Initially, the instantiation count is equalto zero (N=0). At step 124, the system obtains an instantiation of abiometric image. Features (fingerprint minutiae) of the biometric imageare extracted (step 126) from the image instantiation. The instantiationcount is incremented (N=N+1). The extracted feature data for theinstantiation N are stored (step 128) in memory.

To determine whether to continue the process of feature extraction, thecurrent instantiation count (N) is compared (step 130) to a value, whichcorresponds to the number of instantiations to be used in the generationof the data record. This particular number of instantiations can bepredetermined or established dynamically (e.g., the user can continue toperform one more perturbation until further iterations appear to behaving little or no influence on the output results). If this limit hasnot yet been reached, a simulated perturbation is applied (step 132) tothe input image, and the process resumes at step 124, where anotherimage instantiation is obtained. Simulated perturbations applyimage-processing techniques to an image to simulate a change. Subsequentimage perturbations can be applied to the original input image or to anyimage derived from the original input image by virtue of a perturbation.

When the number of instantiations reaches the limit, the feature dataextracted from the N instantiations are merged (step 134), for example,as described in connection with FIG. 7. In response to this merged data,the biometric data record (e.g., a fingerprint template) is generated(step 136). The biometric data record can be stored in a database,making it available for identity matching purposes. The various imagesthemselves, from which the biometric data record derives, can bepersistently saved or discarded.

FIG. 6 shows another embodiment of a process 150 for extracting minutiaeby applying physical perturbations to the biometric identity duringreal-time image acquisition. Although described with reference tofingerprint, the principles of the process may extend to other types ofbiometric identities, such as eyes, handprints, thumbprints, palmprints, footprints, and faces. At step 152, a scanning device receives afinger. The instantiation count is initially equal to zero (N=0). Animage instantiation of the finger is acquired (step 154. The biometricfeature extraction device extracts (step 156) fingerprint features fromthe image instantiation, and the instantiation count increments (N=N+1).The biometric feature extraction device stores (step 158) the extractedfingerprint feature data for the instantiation N in memory. Theinstantiation count is compared (step 160) with a limit to determinewhether another instantiation is to be used. As in FIG. 5, this limitcan be predetermined or established dynamically. If the limit has notbeen reached, a physical perturbation is applied (step 162) to thefinger. Examples of physical perturbations include, but are not limitedto, finger movement, varied pressure applied by the finger, pupildilation of an iris of an eye in response to modulated light directed atthe eye, or the like. Other examples of perturbations can be to alterone or more parameters of a sensor used to acquire the images of thebiometric identity, including, but not limited to, illuminationintensity and acoustic signal strength. The process 150 continues atstep 154, where another instantiation is obtained based on the image ofthe perturbed finger.

When the limit has been reached, the fingerprint feature data extractedfrom N instantiations are merged (step 164), for example, as describedin connection with FIG. 7. A fingerprint template (i.e., biometric datarecord) is generated (step 136) from the merged fingerprint featuredata. The fingerprint template can be stored in a database, availablefor identity fingerprint matching purposes. The various imagesthemselves, from which the fingerprint template derives, can bepersistently saved or discarded.

An important step of a biometric feature extraction process based onmultiple instantiations of an image is the merging of the featureextraction data into a single biometric data record. FIG. 7 shows anembodiment of a process 200 for merging extraction data. To merge thedata acquired from multiple perturbation feature extractions, a counteris associated with each minutia point (also referred to herein as aspecific instance of a biometric feature). At step 202, minutia pointsare extracted from the initial image. The identities of found minutiaepoints (m=1 to M) and their locations (point_(m)) are stored (step 204)in a main initial list. A counter is associated (step 206) with eachminutia, with all minutia counters initialized to 1. A perturbation isperformed (step 208), and minutia points are extracted from theperturbed image. The locations (point_(k)) of these K extracted minutiaeare stored (step 210).

With each perturbation, one of two events occurs: 1) the same minutiapoint is found again; or 2) a new minutia point is found. To determinewhether a found K minutia point (point_(k)) is the same minutia point asa minutia point currently in the main list, the locations (point_(m)) ofthe M minutiae are compared (step 212) with the location (point_(k)) ofthe found K minutiae, to find the closest point_(m) to that point_(k).At step 214, the same minutia point is deemed to have been found for agiven point_(k) when the point_(k) is within a small radius (dThresh) ofa minutia point_(m) currently in the list. The counter for this sameminutia point_(m) is incremented (step 216). If no minutia point_(m)satisfies this distance criterion, the minutia point_(k)is deemed new.The new minutia point_(k) is (step 218) added to the main list (M=M+1;point_(m)=point_(k)) and its counter initialized to 1. The analysis isrepeated (step 220) for each minutia point_(k) found in the perturbedimage.

At step 224, after the number of perturbations has reached the limit(limit perturb), the minutia points found most frequently fromperturbation to perturbation will accumulate the highest counts. Bysetting a threshold (mergeThreshold), those minutia points with countslower than the threshold are presumably false minutiae, whereas thoseequal to or above the threshold, being those most consistentlyextracted, are considered true minutiae. The minutia points fallingbelow the threshold are removed from the list. This list of minutiaeforms (step 226) the final list of minutiae from which the finalbiometric data record is constructed. Accordingly, the process operatesto eliminate false identifications of minutiae while accentuating trueidentifications. Additionally, this threshold can be predetermined ortuned dynamically by a user.

In addition, the final counts for those minutiae above the threshold canbe used to establish a confidence level for each of the minutia points,with higher counts corresponding to higher levels of confidence thatsuch minutiae are, in fact, true minutiae. Conversely, the counts ofthose minutia points that fall below the threshold can be used toestablish a confidence level for each of the false minutia, with lowercounts corresponding to higher levels of confidence that such minutiaeare actually false minutiae (or with higher counts below the thresholdcorresponding to lower levels of confidence that such minutiae areactually false minutiae).

FIG. 8 shows an embodiment of a biometric feature extraction system 250having a biometric feature extraction device 252 comprised of aprocessor 254, memory 256, and a minutia extractor module 258 incommunication over a communication bus 260. The biometric featureextraction device 252 has an I/O module 262 for receiving input in theform of digital images from various sources including an imageacquisition device 264, such as a scanner, and storage 266. Under thecontrol of the processor 254, the minutia extractor module 258 finds andextracts minutiae from digital images received by the biometric featureextraction device 252 through the I/O module 262. In one embodiment, theminutia extractor module 258 is adapted for fingerprint analysis, todetect and extract specific fingerprint features.

A user can supply commands to the biometric feature extraction device252 through a user interface 268. Graphic results produced by thebiometric feature extraction device 252 can be output to an outputdevice 270, such as a display screen, printer (which may or may not bepart of the device 252). Data records (e.g., fingerprint data templates)produced by the biometric feature extraction device 252 can be stored inthe storage 266. The biometric feature extraction device 252 can alsoinclude an image-processing module 272 configured to applyimage-processing techniques to digital images in order to producesynthetic perturbations of an image.

The described methods can be implemented on an image-processing device,fingerprint-processing device, or the like, or on a separate programmedgeneral-purpose computer having image processing capabilities.Additionally, the methods of this invention can be implemented on aspecial-purpose computer, a programmed microprocessor or microcontrollerand peripheral integrated circuit element(s), an ASIC or otherintegrated circuit, a digital signal processor, a hard-wired electronicor logic circuit such as discrete element circuit, a programmable logicdevice such as PLD, PLA, FPGA, PAL, or the like. In general, any devicecapable of implementing a state machine that is in turn capable ofimplementing the proposed methods herein can be used to implement theimage processing system according to this invention.

Furthermore, the disclosed methods may be readily implemented insoftware using objector object-oriented software developmentenvironments that provide portable source code that can be used on avariety of computer or workstation platforms. Alternatively, thedisclosed system may be implemented partially or fully in hardware usingstandard logic circuits or a VLSI design. Whether software or hardwareis used to implement the systems in accordance with this invention isdependent on the speed and/or efficiency requirements of the system, theparticular function, and the particular software or hardware systems ormicroprocessor or microcomputer systems being utilized. The methodsillustrated herein however can be readily implemented in hardware and/orsoftware using any known or later developed systems or structures,devices and/or software by those of ordinary skill in the applicable artfrom the functional description provided herein and with a general basicknowledge of the computer and image processing arts.

Moreover, the disclosed methods may be readily implemented in softwareexecuted on programmed general-purpose computer, a special purposecomputer, a microprocessor, or the like. In these instances, the systemsand methods of this invention can be implemented as program embedded onpersonal computer such as JAVA® or CGI script, as a resource residing ona server or graphics workstation, as a routine embedded in a dedicatedfingerprint processing system, as a plug-in, or the like. The system canalso be implemented by physically incorporating the system and methodinto a software and/or hardware system, such as the hardware andsoftware systems of an image processor.

It is, therefore, apparent that there has been provided systems andmethods for improving the robustness, accuracy, and repeatability ofminutia extraction from digital fingerprint images by combininginformation from the analysis of multiple instantiations of the samefingerprint identity. While these principles have been described inconjunction with a number of embodiments, it is evident that manyalternatives, modifications, and variations would be or are apparent tothose of ordinary skill in the applicable arts. Accordingly, it isintended to embrace all such alternatives, modifications, equivalents,and variations that are within the spirit and scope of the invention.

What is claimed is: 1-25. (canceled)
 26. A computer readable informationstorage media having stored thereon instructions, that when executed byone or more processors, cause to be performed a method to extractbiometric features from images of a biometric identity comprising:obtaining or generating multiple variations of a single imageinstantiation for the biometric identity to one or more of: increasedetection of true minutiae, reduce detection of false minutiae, and/orimprove consistency feature extraction, wherein a limit governs how manyof the multiple variations are used; identifying, using a biometricfeature extraction device including a memory interconnected with aprocessor, the processor programmed to run computer readable programcode, specific instances of biometric features in each of the multiplevariations of the single image instantiation of the same biometricidentity; and statistically merging common feature data, correspondingto the specific instances of biometric features identified anddetermined to be at a same location in the multiple different variationsof the single image instantiation of the same biometric identity, into asingle biometric template representation of the biometric identity,wherein the merging of the common feature data includes: identifyingfound minutia points, associating a counter with each found minutiapoint, and storing a location of each found minutia point, and when thelimit has been reached, determining, based on a threshold compared to acount, which are true minutiae and removing false minutiae.
 27. Themedia of claim 26, further comprising counting the specific instances ofbiometric features identified and determined to be at the same locationin the multiple different variations of the single image instantiationof the same biometric identity.
 28. The media of claim 26, whereinobtaining multiple variations further includes physically changing aposition of the biometric identity during image acquisition.
 29. Themedia of claim 26, wherein obtaining multiple variations furtherincludes physically deforming the biometric identity during imageacquisition.
 30. The media of claim 26, wherein obtaining multiplevariations further includes altering one or more parameters of a sensorused to acquire the multiple images.
 31. The media of claim 26, furthercomprising obtaining multiple variations further includes simulating achange in a previously acquired image of the biometric identity usingthe image processing technique.
 32. The media of claim 26, wherein oneof the multiple images is acquired in response to physically perturbinga position of the biometric identity and another of the multiple imagesis acquired in response to simulating a change in a previously acquiredimage of the biometric identity using an image processing technique. 33.The media of claim 26, further comprising: maintaining the count foreach of the same identified specific instances of a biometric feature,the count for a given specific instance corresponding to a number of themultiple different images in which that specific instance is identified;and generating the biometric data record comprised of each specificinstance of a biometric feature having a count that meets a thresholdcriterion.
 34. The media of claim 33, further comprising associating aconfidence level with one or more of the identified specific instancesof biometric features based on the count associated with each identifiedspecific instance of that one or more of the identified specificinstances.
 35. The media of claim 26, wherein the identified specificinstances of biometric features are minutiae of fingerprints.
 36. Asystem for extracting biometric features from images of a biometricidentity comprising: means for obtaining or generating multiplevariations of a single image instantiation for the biometric identity toone or more of: increase detection of true minutiae, reduce detection offalse minutiae, and/or improve consistency feature extraction, wherein alimit governs how many of the multiple variations are used; means foridentifying, using a biometric feature extraction device including amemory interconnected with a processor, the processor programmed to runcomputer readable program code, specific instances of biometric featuresin each of the multiple variations of the single image instantiation ofthe same biometric identity; and means for statistically merging commonfeature data, corresponding to the specific instances of biometricfeatures identified and determined to be at a same location in themultiple different variations of the single image instantiation of thesame biometric identity, into a single biometric template representationof the biometric identity, wherein the merging of the common featuredata includes: identifying found minutia points, associating a counterwith each found minutia point, and storing a location of each foundminutia point, and when the limit has been reached, determining, basedon a threshold compared to a count, which are true minutiae and removingfalse minutiae.
 37. The system of claim 36, further comprising means forcounting the specific instances of biometric features identified anddetermined to be at the same location in the multiple differentvariations of the single image instantiation of the same biometricidentity.
 38. The system of claim 36, wherein obtaining multiplevariations further includes physically changing a position of thebiometric identity during image acquisition.
 39. The system of claim 36,wherein obtaining multiple variations further includes physicallydeforming the biometric identity during image acquisition.
 40. Thesystem of claim 36, wherein obtaining multiple variations furtherincludes altering one or more parameters of a sensor used to acquire themultiple images.
 41. The system of claim 36, further comprising meansfor obtaining multiple variations further includes simulating a changein a previously acquired image of the biometric identity using the imageprocessing technique.
 42. The system of claim 36, wherein one of themultiple images is acquired in response to physically perturbing aposition of the biometric identity and another of the multiple images isacquired in response to simulating a change in a previously acquiredimage of the biometric identity using an image processing technique. 43.The system of claim 36, further comprising: means for maintaining thecount for each of the same identified specific instances of a biometricfeature, the count for a given specific instance corresponding to anumber of the multiple different images in which that specific instanceis identified; and means for generating the biometric data recordcomprised of each specific instance of a biometric feature having acount that meets a threshold criterion.
 44. The system of claim 43,further comprising means for associating a confidence level with one ormore of the identified specific instances of biometric features based onthe count associated with each identified specific instance of that oneor more of the identified specific instances.
 45. The system of claim36, wherein the identified specific instances of biometric features areminutiae of fingerprints.